SP-MoMamba: Superpixel-driven Mixture of State Space Experts for Efficient Image Super-Resolution

• URL: http://arxiv.org/abs/2605.25892v1
• Date: Mon, 25 May 2026 14:19:59 GMT
• Title: SP-MoMamba: Superpixel-driven Mixture of State Space Experts for Efficient Image Super-Resolution
• Abstract summary: State space models (SSMs) have emerged as a powerful paradigm for efficient single-image super-resolution (SR)<n>We propose textbfSP-MoMamba, a superpixel-driven mixture of state space experts for content-aware SR.<n>Our core idea is to transform the traditional rigid scanning into a textbfsemantic-level interaction by treating superpixels as fundamental units.
• Score: 99.56535031145211
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: State space models (SSMs) have emerged as a powerful paradigm for efficient single-image super-resolution (SR) due to their linear complexity and long-range modeling capabilities. However, existing Mamba-based methods typically rely on data-agnostic rigid scanning, which reshapes 2D images into 1D sequences over a fixed grid, inevitably disrupting spatial-semantic topology and introducing artifacts. Inspired by the \textbf{Gestalt perceptual grouping theory}, we propose \textbf{SP-MoMamba}, a superpixel-driven mixture of state space experts designed for content-aware SR. Our core idea is to transform the traditional rigid scanning into a \textbf{semantic-level interaction} by treating superpixels as fundamental units. Specifically, we introduce the \textbf{Superpixel-driven State Space Model (SP-SSM)}, which compresses semantically homogeneous regions into high-order tokens to preserve global topological consistency. To address the conflict between fixed scanning scales and diverse semantic granularities, we develop the \textbf{Multi-Scale Superpixel Mixture of State Space Experts (MSS-MoE)}. This module utilizes a dynamic routing mechanism to adaptively assign scale-specific experts, effectively capturing multi-scale textures while reducing computational redundancy. Furthermore, to prevent the loss of high-frequency details during global abstraction, we introduce a \textbf{Local Spatial Modulation Expert (LSME)} to complement the global modeling, ensuring a precise reconstruction of sharp edges and fine structures. Extensive experiments on standard benchmarks demonstrate that SP-MoMamba achieves superior reconstruction fidelity and a more favorable efficiency-performance trade-off compared to state-of-the-art efficient SR methods.

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